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A Head-to-Head Comparison of the EQ-5D-5L and AQoL-8D Multi-Attribute Utility Instruments in Patients Who Have Previously Undergone Bariatric Surgery

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Abstract

Background

Psychosocial health status is an important and dynamic outcome for bariatric/metabolic surgery patients, as acknowledged in recent international standardised outcomes reporting guidelines. Multi-attribute utility-instruments (MAUIs) capture and assess an individual’s health-related quality-of-life (HRQoL) within a single valuation, their utility. Neither MAUIs nor utilities were discussed in the guidelines. Many MAUIs (e.g. EQ-5D) target physical health. Not so the AQoL-8D.

Objectives

Our objective was to explore agreement between, and suitability of, the EQ-5D-5L and AQoL-8D for assessing health state utility, and to determine whether either MAUI could be preferentially recommended for metabolic/bariatric surgery patients.

Methods

Utilities for post-surgical private-sector patients (n = 33) were assessed using both instruments and summary statistics expressed as mean [standard deviation (SD)] and median [interquartile range (IQR)]. Interchangeability of the MAUIs was assessed with Bland–Altman analysis. Discriminatory attributes were investigated through floor/ceiling effects and dimension-to-dimension comparisons. Spearman’s rank measured associations between the instruments’ utility values and with the body mass index (BMI).

Results

Mean (SD) EQ-5D-5L utility value was 0.84 (0.15) and median 0.84 (IQR 0.75–1.00). Mean (SD) AQoL-8D utility value was 0.76 (0.17) and median 0.81 (IQR 0.63–0.88). Spearman’s rank was r = 0.68; (p < 0.001); however, Bland–Altman analysis revealed fundamental differences. Neither instrument gave rise to floor effects. A ceiling effect was observed with the EQ-5D-5L, with 36 % of participants obtaining a utility value of 1.00 (perfect health). These same participants obtained a mean utility of 0.87 on the AQoL-8D, primarily driven by the mental-super-dimension score (0.52).

Conclusions

The AQoL-8D preferentially captures psychosocial aspects of metabolic/bariatric surgery patients’ HRQoL. We recommend the AQoL-8D as a preferred MAUI for these patients given their complex physical/psychosocial needs.

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References

  1. Gortmaker SL, Swinburn BA, Levy D, Carter R, Mabry PL, Finegood DT, et al. Changing the future of obesity: science, policy, and action. Lancet. 2011;378(9793):838–47.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Angrisani L, Santonicola A, Iovino P, Formisano G, Buchwald H, Scopinaro N. Bariatric surgery worldwide 2013. Obes Surg. 2015;25(10):1822–32.

    Article  CAS  PubMed  Google Scholar 

  3. Buchwald H, Oien DM. Metabolic/bariatric surgery worldwide 2011. Obes Surg. 2013;23(4):427–36.

    Article  PubMed  Google Scholar 

  4. Lindekilde N, Gladstone BP, Lübeck M, Nielsen J, Clausen L, Vach W, et al. The impact of bariatric surgery on quality of life: a systematic review and meta-analysis. Obes Rev. 2015;16(8):639–51.

    Article  CAS  PubMed  Google Scholar 

  5. Herpertz S, Müller A, Burgmer R, Crosby RD, de Zwaan M, Legenbauer T. Health-related quality of life and psychological functioning 9 years after restrictive surgical treatment for obesity. Surg Obes Relat Dis. 2015;11(6):1361–70. doi:10.1016/j.soard.04.008.

    Article  PubMed  Google Scholar 

  6. Karmali S. The impact of bariatric surgery on psychological health. J Obes. 2013. doi:10.1155/2013/837989 (Epub 28 Mar 2013).

    Google Scholar 

  7. Burgmer R, Legenbauer T, Müller A, de Zwaan M, Fischer C, Herpertz S. Psychological outcome 4 years after restrictive bariatric surgery. Obes Surg. 2014;24(10):1670–8.

    Article  PubMed  Google Scholar 

  8. Brethauer SA, Kim J, El Chaar M, Papasavas P, Eisenberg D, Rogers A, et al. Standardized outcomes reporting in metabolic and bariatric surgery. Surg Obes Rel Dis. 2015;11(3):489–506.

    Article  Google Scholar 

  9. Brethauer S, Kim J, el Chaar M, Papasavas P, Eisenberg D, Rogers A, et al. Standardized outcomes reporting in metabolic and bariatric surgery. Obes Surg. 2015;25(4):587–606.

    Article  PubMed  Google Scholar 

  10. Khan MA, Richardson J, O’Brien P. The effect of obesity upon health related quality of life (HRQoL): a comparison of the AQoL-8D and SF-36 instruments. Farmeconomia Health Economics Therapeutic Pathways. 2012;13(2):69–82.

    Article  Google Scholar 

  11. Drummond MF, Sculpher M, Torrance G, O’Brien B, Stoddart G. Methods for the economic evaluation of health care programmes. 3rd ed. New York: Oxford University Press; 2005.

    Google Scholar 

  12. Richardson J, Iezzi A, Khan MA, Maxwell A. Validity and reliability of the Assessment of Quality of Life (AQoL)-8D multi-attribute utility instrument. Patient. 2014;7(1):85–96.

    Article  PubMed  Google Scholar 

  13. Skinner EH, Denehy L, Warrillow S, Hawthorne G. Comparison of the measurement properties of the AQoL and SF-6D in critical illness. Critic Care Resusc. 2013;15(3):205.

    Google Scholar 

  14. Clarke PM, Hayes AJ, Glasziou PG, Scott R, Simes J, Keech AC. Using the EQ-5D index score as a predictor of outcomes in patients with type 2 diabetes. Med Care. 2009;47(1):61–8.

    Article  PubMed  Google Scholar 

  15. Richardson J, Iezzi A, Khan MA. Why do multi-attribute utility instruments produce different utilities: the relative importance of the descriptive systems, scale and ‘micro-utility’effects. Qual Life Res. 2015;24:1–9.

    Article  Google Scholar 

  16. Chen G, Iezzi A, McKie J, Khan MA, Richardson J. Diabetes and quality of life: comparing results from utility instruments and Diabetes-39. Diabetes Res Clin Pract. 2015;109(2):326–33. doi:10.1016/j.diabres.2015.05.011.

    Article  PubMed  Google Scholar 

  17. Chen G, Khan MA, Iezzi A, Ratcliffe J, Richardson J. Mapping between 6 multiattribute utility instruments. Med Decis Making. 2015. doi:10.1177/0272989x15578127 (Epub 3 Apr 2015).

    Google Scholar 

  18. National Institute for Health and Care Excellence. Process and methods guides: guide to the methods for technology appraisal 2013. London: NICE; 2013. https://www.nice.org.uk/article/pmg9/resources/non-guidance-guide-to-the-methods-of-technology-appraisal-2013-pdf. Accessed 15 June 2015.

  19. Richardson J, McKie J, Bariola E. Review and critique of health related multi attribute utility instruments. Melbourne: Monash University, Business and Economics, Centre for Health Economics; 2011.

    Google Scholar 

  20. Yang Y, Rowen D, Brazier J, Tsuchiya A, Young T, Longworth L. An exploratory study to test the impact on three “bolt-on” items to the EQ-5D. Value Health. 2015;18(1):52–60.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Lin F-J, Longworth L, Pickard AS. Evaluation of content on EQ-5D as compared to disease-specific utility measures. Qual Life Res. 2013;22(4):853–74.

    Article  PubMed  Google Scholar 

  22. Brazier J, Roberts J, Tsuchiya A, Busschbach J. A comparison of the EQ-5D and SF-6D across seven patient groups. Health Econ. 2004;13(9):873–84.

    Article  PubMed  Google Scholar 

  23. Herdman M, Gudex C, Lloyd A, Janssen M, Kind P, Parkin D, et al. Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L). Qual Life Res. 2011;20(10):1727–36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Richardson. J. Increasing the sensitivity of the AQoL inventory for the evaluation of interventions affecting mental health. Melbourne: Monash University, Centre for Health Economics; 2011.

  25. Holland R, Smith RD, Harvey I, Swift L, Lenaghan E. Assessing quality of life in the elderly: a direct comparison of the EQ-5D and AQoL. Health Econ. 2004;13(8):793–805.

    Article  PubMed  Google Scholar 

  26. Dolan P. Modeling valuations for EuroQol health states. Med Care. 1997;35(11):1095–108.

    Article  CAS  PubMed  Google Scholar 

  27. Richardson J, Sinha K, Iezzi A, Khan M. Modelling utility weights for the Assessment of Quality of Life (AQoL)-8D. Qual Life Res. 2014;23(8):2395–404.

    Article  PubMed  Google Scholar 

  28. Richardson J, Atherton Day N, Peacock S, Iezzi A. Measurement of the quality of life for economic evaluation and the Assessment of Quality of Life (AQoL) Mark 2 instrument. Aust. Econ Rev. 2004;37(1):62–88.

    Google Scholar 

  29. Jia Y, Cui F, Li L, Zhang D, Zhang G, Wang F, et al. Comparison between the EQ-5D-5L and the EQ-5D-3L in patients with hepatitis B. Qual Life Res. 2014;23(8):2355–63.

    Article  CAS  PubMed  Google Scholar 

  30. Janssen M, Pickard AS, Golicki D, Gudex C, Niewada M, Scalone L, et al. Measurement properties of the EQ-5D-5L compared to the EQ-5D-3L across eight patient groups: a multi-country study. Qual Life Res. 2013;22(7):1717–27.

    Article  CAS  PubMed  Google Scholar 

  31. Turner N, Campbell J, Peters TJ, Wiles N, Hollinghurst S. A comparison of four different approaches to measuring health utility in depressed patients. Health Qual Life Outcomes. 2013;11:81.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Culyer AJ. Encyclopaedia of Health Economics. 1st ed. : vol. 2. 1st ed. Oxford: Elsevier Science; 2014. p. 353.

    Google Scholar 

  33. Stevens KJ. How well do the generic multi-attribute utility iIncorporate patient and public views into their descriptive systems? Patient. 2015. doi:10.1007/s40271-015-0119-y (Epub 8 Feb 2015).

    Google Scholar 

  34. van Hout B, Janssen M, Feng Y-S, Kohlmann T, Busschbach J, Golicki D, et al. Interim scoring for the EQ-5D-5L: mapping the EQ-5D-5L to EQ-5D-3L value sets. Value Health. 2012;15(5):708–15.

    Article  PubMed  Google Scholar 

  35. Marra CA, Woolcott JC, Kopec JA, Shojania K, Offer R, Brazier JE, et al. A comparison of generic, indirect utility measures (the HUI2, HUI3, SF-6D, and the EQ-5D) and disease-specific instruments (the RAQoL and the HAQ) in rheumatoid arthritis. Soc Sci Med. 2005;60(7):1571–82.

    Article  PubMed  Google Scholar 

  36. Bland JM, Altman DG. Applying the right statistics: analyses of measurement studies. Ultrasound Obstet Gynecol. 2003;22(1):85–93.

    Article  CAS  PubMed  Google Scholar 

  37. Sullivan PW, Lawrence WF, Ghushchyan V. A national catalog of preference-based scores for chronic conditions in the United States. Med Care. 2005;43(7):736–49.

    Article  PubMed  Google Scholar 

  38. Cunillera O, Tresserras R, Rajmil L, Vilagut G, Brugulat P, Herdman M, et al. Discriminative capacity of the EQ-5D, SF-6D, and SF-12 as measures of health status in population health survey. Qual Life Res. 2010;19(6):853–64. doi:10.1007/s11136-010-9639-z.

    Article  PubMed  Google Scholar 

  39. Ribaric G, Buchwald JN, d’Orsay G, Daoud F. 3-year real-world outcomes with the Swedish adjustable gastric band in France. Obes Surg. 2013;23(2):184–96.

    Article  CAS  PubMed  Google Scholar 

  40. Ackroyd R, Mouiel J, Chevallier JM, Daoud F. Cost-effectiveness and budget impact of obesity surgery in patients with type-2 diabetes in three European countries. Obes Surg. 2006;16(11):1488–503.

    Article  PubMed  Google Scholar 

  41. Lin VW, Wong ES, Wright A, Flum DR, Garrison LP Jr, Alfonso-Cristancho R, et al. Association between health-related quality of life and body mass after adjustable gastric banding: a nonlinear approach. Value Health. 2013;16(5):823–9.

    Article  PubMed  Google Scholar 

  42. Pattanaphesaj J, Thavorncharoensap M. Measurement properties of the EQ-5D-5L compared to EQ-5D-3L in the Thai diabetes patients. Health Qual Life Outcomes. 2015;13(1):14. doi:10.1186/s12955-014-0203-3.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Pan C-W, Sun H-P, Wang X, Ma Q, Xu Y, Luo N, et al. The EQ-5D-5L index score is more discriminative than the EQ-5D-3L index score in diabetes patients. Qual Life Res. 2014;24(7):1767–74.

    Article  PubMed  Google Scholar 

  44. Yang F, Lau T, Lee E, Vathsala A, Chia KS, Luo N. Comparison of the preference-based EQ-5D-5L and SF-6D in patients with end-stage renal disease (ESRD). Eur J Health Econ. 2015;16(9):1019–26. doi:10.1007/s10198-014-0664-7.

    Article  PubMed  Google Scholar 

  45. Scalone L, Ciampichini R, Fagiuoli S, Gardini I, Fusco F, Gaeta L, et al. Comparing the performance of the standard EQ-5D 3L with the new version EQ-5D 5L in patients with chronic hepatic diseases. Qual Life Res. 2013;22(7):1707–16.

    Article  PubMed  Google Scholar 

  46. Tayyem R, Ali A, Atkinson J, Martin CR. Analysis of health-related quality-of-life instruments measuring the impact of bariatric surgery: systematic review of the instruments used and their content validity. Patient. 2011;4(2):73–87.

    Article  PubMed  Google Scholar 

  47. Tayyem R, Atkinson J, Martin C. Development and validation of a new bariatric-specific health-related quality of life instrument “bariatric and obesity-specific survey (BOSS)”. J Postgrad Med. 2014;60(4):357–61.

    Article  CAS  PubMed  Google Scholar 

  48. Mihalopoulos C, Chen G, Iezzi A, Khan MA, Richardson J. Assessing outcomes for cost-utility analysis in depression: comparison of five multi-attribute utility instruments with two depression-specific outcome measures. Br J Psychiatry. 2014;205(5):390–7.

    Article  PubMed  Google Scholar 

  49. Scalone L, Cortesi PA, Ciampichini R, Cesana G, Mantovani LG. Health related quality of life norm data of the Italian general population: results using the EQ-5D-3L and EQ-5D-5L instruments. Epidemiol Biostat Public Health. 2015;12(3):e11457-1-15.

  50. Norman R, Cronin P, Viney R. A pilot discrete choice experiment to explore preferences for EQ-5D-5L health states. Appl Health Econ Health Policy. 2013;11(3):287–98.

    Article  PubMed  Google Scholar 

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Authorship

Julie Campbell contributed to study design, data verification and analysis, manuscript preparation and final approval. Andrew Palmer contributed to study design, manuscript review and final approval. Alison Venn contributed to study design, manuscript review and final approval. Melanie Sharman contributed to data collection and verification, manuscript review and final approval. Petr Otahal contributed to statistical analysis, manuscript review and final approval. Amanda Neil contributed to study design, data analysis, manuscript preparation and final approval. Amanda Neil is the overall guarantor of the submission.

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Correspondence to Amanda Neil.

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Disclosure of potential conflicts of interest

This work was supported by a National Health and Medical Research Council (NHMRC) Partnership Project Grant (APP1076899). AV is supported by a NHMRC Research Fellowship. AN is supported by a Select Foundation Research Fellowship.

Research involving human participants

Ethics approval was granted by the University of Tasmania’s Health and Medical Human Research Ethics Committees.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Conflict of interest

The authors Julie A. Campbell, Andrew J. Palmer, Alison Venn, Melanie Sharman, Petr Otahal and Amanda Neil declare that they have no conflicts of interest.

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Campbell, J.A., Palmer, A.J., Venn, A. et al. A Head-to-Head Comparison of the EQ-5D-5L and AQoL-8D Multi-Attribute Utility Instruments in Patients Who Have Previously Undergone Bariatric Surgery. Patient 9, 311–322 (2016). https://doi.org/10.1007/s40271-015-0157-5

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